Calibration on internal thread gauge on a coordinate measuring machine

This paper starts at first describes in detail the analytical model developed by (Seneviratne et al.,1992; Ngemoh,1997; Seneviratne et al., 2001) and shows experimental setup for measuring torque vs insertion depth signature signals , then, justified by the good correspondence between model and experimental data, shows an integrated approach developed by the authors (Klingajay & Giannoccaro, 2003) Klingajay et al.,2003), for estimating some physical parameters of screw insertion. This approach is developed in Matlab development creating a Graphic User Interface (GUI) that manage signals from the sensors, estimates these required parameters of the insertion, with the aim of driving automatically screw insertion. Test results about the possibility of estimating four parameters of this model are shown in this paper using a non-linear optimization technique (Least Square optimisation technique). This technique works well considering the complexity of the model equations (Appendix 1) like shown by the authors (Klingajay & Giannoccaro, 2003) for this particular kind of estimation

The monitoring of autonomous threaded fastening using least square base on five parameters

IADAT Journal of Advanced Technology on Automation,Control and Instrumentatio

The automated threaded fastening based on on-line identification

The principle of the thread fastenings have been known and used for decades with the purpose of joining one component to another. Threaded fastenings are popular because they permit easy disassembly for maintenance, repair, relocation and recycling. Screw insertions are typically carried out manually. It is a difficult problem to automat. As a result there is very little published research on automating threaded fastenings, and most research on automated assembly focus on the peg-in-hole assembly problem. This paper investigates the problem of automated monitoring of the screw insertion process. The monitoring problem deals with predicting integrity of a threaded insertion, based on the torque vs. insertion depth curve generated during the insertions. The authors have developed an analytical model to predict the torque signature signals during self-tapping screw insertions. However, the model requires parameters on the screw dimensions and plate material properties are difficult to measure. This paper presents a study on on-line identification during screw fastenings. An identification methodology for two unknown parameter estimation during a self-tapping screw insertion process is presented. It is shown that friction and screw properties required by the model can be reliably estimated on-line. Experimental results are presented to validate the identification procedure